ESM 266: Passive microwave remote sensing

1
ESM 266 Passive microwave remote sensing

• Jeff Dozier

2
Frequency-wavelength relation

• Generally in the microwave part of the spectrum
  we use frequency instead of wavelength
• Typically measured in s1, called Hertz (Hz)
• Most often Gigahertz (GHz) 109Hz

3
Microwave band codes
4
Advantages of passive microwave remote sensing

• Sees through clouds at lower frequencies
• Long heritage, various instruments since 1978
• Emissivity sensitive to state of surface,
  particularly moisture
• Soil moisture
• Snow-water equivalent
• (water is 80 as absorptive as ice at these
  frequencies, whereas in visible through infrared,
  water and ice have similar absorption
  coefficients)
• But, because of small amount of energy emitted,
  pixel size must be large

5
Characteristics of major passive microwave
instruments
6
Planck equation frequency form
7
Planck equation f(frequency,Temperature)
Planck radiation at20,000 GHz is 36,000?greater
than at 37 GHz,so pixels at lower
frequenciesmust be bigger
8
Rayleigh-Jeans approximation to Planck equation

• Linear relation between Planck radiation and
  frequency, on a log-log plot, suggests a power
  function

http//en.wikipedia.org/wiki/Taylor_series
9
The really useful simplification involves
emissivity and brightness temperature

• Emissivity varies with frequency and polarization

10
EOS Aqua satellite (afternoon overpass)

• Six instruments, 3 in microwave
• AIRS
• CERES
• AMSR-E, Advanced Microwave Scanning Radiometer
  for EOS
• AMSR also flies on ADEOS-II (Japanese)
• AMSU, Advanced Microwave Sounding Unit
• HSB, Humidity Sounder for Brazil
• MODIS

11
Sea ice from AMSR, Sea of Okhotsk
Sea ice, 18 Jan 2003
Motion vectors, 10hrs
12
AMSR-E products

• 6 frequencies, 12 channels (dual polarization),
  from 6.9-89 GHz
• Precipitation rate
• Cloud water
• Water vapor
• Sea-surface winds
• Sea-surface temperature
• Sea ice
• Snow-water equivalent
• Soil moisture

13
Aqua Prior to Launch
Earth-viewing side
Space-viewing side
14
Aquas Delta II Rocket
(photos by Bill Ingalls)
15
The Aqua Sounding Suite
Humidity Sounder for Brazil (HSB)
Atmospheric Infrared Sounder (AIRS)
Advanced Microwave Sounding Unit (AMSU two units)
AMSU A1
AMSU A2
16
Key Improvements Anticipated from AIRS/AMSU/HSB
Data

• Atmospheric temperatures to accuracies of 1 K in
  1-km layers.
• Atmospheric humidities to
  10 in 2-km layers.
• Resultant improved
  weather forecasting.

Launch of a radiosonde
17
Sample AIRS Infrared Spectra
a. Data from all 2378 AIRS infrared channels for
one footprint off the west coast of South Africa,
June 13, 2002, 130 UTC.
500 1000
1500 2000
2500 wavenumber
(cm-1) 20 10
6.7
5 4
wavelength (?m)
b. Detail showing the leftmost 128 of the 2378
channels in plot a.
18
Texas Thunderstorms as Seen in AMSU and HSB
Imagery, June 16, 2002
AMSU Ch. 2 (31.4 GHz)
AMSU Ch. 3 (50.3 GHz)
AMSU Ch. 4 (52.8 GHz)
AMSU Ch. 5 (53.94 GHz)
HSB Ch. 2 (150 GHz)
HSB Ch. 3 (1831 GHz)
HSB Ch. 4 (1833 GHz)
HSB Ch. 5 (1837 GHz)
19
Rain Rate Images from AMSU/HSB June 16, 2002
Scandinavia
South central U.S.
20
Hurricane Alma, west of Mexico, May 29, 2002,
from HSB and AIRS
HSB 150 GHz data
AIRS Visible/Near IR data
(images courtesy of the AIRS Science Team)
21
Surface Conditions and Moisture Streams in the
Vicinity of Northern Europe, July 20, 2002
Surface Conditions from AMSU
Moisture Streams from HSB
22
Advanced Microwave Scanning Radiometer for EOS
(AMSR-E)
23
Global Sea Surface Temperatures from AMSR-E,
June 2-4, 2002
(image courtesy of NASDA)
24
Typhoon in the East China Sea July 4, 2002, from
AMSR-E
Japan
China
AMSR-E image, 226 a.m. Japan Standard Time
(JST).
Taiwan
Philippines
25
Precipitation over the Eastern U.S. and Vicinity,
from AMSR-E and the TRMM Microwave Imager (TMI),
June 5, 2002
TMI Total Rainfall
AMSR-E Total Rainfall
(images courtesy of Chris Kummerow and Bob Adler)
26
Global Sea Ice Coverage June 2-4, 2002 (top) and
July 21-22, 2002 (bottom), from AMSR-E
27
Sample Record to be Extended with the AMSR-E
Data North Polar Sea Ice Extents
Ice extent deviations from the Nimbus 7 SMMR and
DMSP SSMI
(extended from Parkinson et al., 1999)
28
AMSR products and algorithms

• AMSR Algorithm Theoretical Basis Documents
• Land surface parameters
• Soil moisture, surface temperature, vegetation
  water
• Brightness temperatures
• Ocean
• Sea-surface temperature, wind speed, water vapor,
  cloud water
• Rainfall (works best over oceans)
• Sea ice
• Concentration, temperature, snow on sea ice
• Nice graphic from New York Times on sea ice
  decline
• Snow water equivalent
• For dry snow, snow reduces apparent brightness
  temperature from soil
• For wet snow, mainly detects that snow is wet